def test_get_facial_landmarks_dlib(self):
image_path = '../assets/examples/example_6.jpg'
image = cv2.imread(image_path)
rect = face_process.get_face_rect(image)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(
image, rect)
landmark_image = face_segmentation.draw_landmark(image, landmarks)
cv2.imwrite('test_facial_landmarks.jpg', landmark_image)
pass
def test_landmarks_rectification(self):
image_path = '../assets/targets/target_10.jpeg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
face_contour_landmarks = landmarks[0:17]
image_size = tuple([image.shape[0], image.shape[1]])
facial_region_index = face_segmentation.get_facial_indices(
landmarks, image_size)
gmm_color = face_segmentation.gmm_color_model(image,
facial_region_index)
rectified_landmarks = landmarks_rectification.landmarks_rectify(
image,
face_contour_landmarks,
gmm_color,
resolution_list=tuple([512]))
image = face_segmentation.draw_landmark(image, face_contour_landmarks)
image = face_segmentation.draw_landmark(image, rectified_landmarks,
(0, 0, 255))
cv2.imwrite('test_rectify.jpg', image)
def test__landmark_rectify(self):
image_path = '../assets/examples/example_8.jpeg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
face_contour_landmarks = landmarks[0:17]
image_size = tuple([image.shape[0], image.shape[1]])
facial_region_index = face_segmentation.get_facial_indices(
landmarks, image_size)
gmm_color = face_segmentation.gmm_color_model(image,
facial_region_index)
rectified_landmark = landmarks_rectification._landmark_rectify(
image,
face_contour_landmarks,
gmm_color,
search_dir='horizontal',
window_size=25)
image = face_segmentation.draw_landmark(image, landmarks)
image = face_segmentation.draw_landmark(image,
rectified_landmark,
color=(0, 0, 255))
cv2.imwrite('test_rectify.jpg', image)
def test_facial_features(self):
image_path = '../assets/targets/target_12.jpg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
_, triangle, triangle_mesh = face_segmentation.get_triangle_landmarks(
landmarks)
triangle, triangle_mesh = makeup_by_facial_features.get_triangle(
landmarks)
image = face_segmentation.draw_landmark(image, landmarks)
for triangle in triangle_mesh:
cv2.polylines(image, [np.asarray(triangle)], True, (0, 255, 255))
cv2.imwrite('facial_features.jpg', image)
pass
def test_transfer_image(self):
image_path = '../assets/examples/example_8.jpeg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
face_rect = face_process.select_face_rect(landmarks)
face_image = image[face_rect['left_top'][1]:face_rect['bottom_right'][1],
face_rect['left_top'][0]:face_rect['bottom_right'][0]]
cv2.imwrite('test.jpg', face_image)
new_image, t = face_process.calc_image_transform(face_rect, image, resolution=512)
cv2.imwrite('test1.jpg', new_image)
new_landmarks = []
for landmark in landmarks:
new_landmarks.append(face_process.calc_transform_pts(landmark, t))
new_image = face_segmentation.draw_landmark(new_image, new_landmarks)
cv2.imwrite('test2.jpg', new_image)
self.assertEquals(True,True)
def test_calc_transform_mat(self):
image_path = '../assets/examples/after-makeup1.jpeg'
image = cv2.imread(image_path)
landmarks = facial_landmarks_detection_dlib.get_facial_landmarks(image)
height, width = image.shape[0], image.shape[1]
origin_resolution = min(height, width)
origin_center = tuple([origin_resolution /2, origin_resolution/2])
image = image[0: origin_resolution, 0:origin_resolution]
new_resolution = 256
image = cv2.resize(image, dsize=(int(new_resolution),int(new_resolution)))
new_center = tuple([new_resolution / 2, new_resolution/ 2])
transform = face_process._calc_transfer_mat(origin_center, new_center, origin_resolution, new_resolution)
new_landmarks = []
for landmark in landmarks:
new_landmarks.append(face_process.calc_transform_pts(landmark, transform))
new_image = face_segmentation.draw_landmark(image, new_landmarks)
cv2.imwrite('test1.jpg', new_image)